Hands-On Machine Learning from Scratch

Develop a Deeper Understanding of Machine Learning Models by Implementing Them from Scratch in Python

This book will guide you on your journey to deeper Machine Learning understanding by developing algorithms in Python from scratch! Learn why and when Machine learning is the right tool for the job and how to improve low performing models!

About the Book

Hello!

Recent developments in the field of Artificial Intelligence made Machine Learning methods usable and efficient in practice. Tools like PyTorch, TensorFlow, Scikit-Learn make it easy to build your first model.

Unfortunately, reading through a Machine Learning/Deep Learning tutorial showing how to use a library will leave you asking - why does it work, when can I use that and how can I improve it? Now what? This book will help you answer those questions!

What is in for you?

- Step-by-step guide on how to approach, visualize and solve data science problems

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About the Author

My name is Venelin and I am thrilled to invite you on a journey through the amazing world of Machine Learning.

I've been working on quite a lot of software projects in the past 10+ years, started my PhD program in Bayesian Statistics and been fascinated by Machine Learning in the past 5+ years (including some industry experience).

Table of Contents

Machine Learning fundamentals

Sick of being a lamer?

What is Machine Learning?

What Learning Algorithms are made of?

Making predictions

Tools of the trade

Ready to start?

Smart Discounts with Logistic Regression

The Data

Making decisions with Logistic regression

The Logistic Regression model

How can we find the parameters for our model?

Bonus — building your own LogisticRegressor

Conclusion

Predicting House Prices with Linear Regression

The Data

Load the data

Exploration — getting a feel for our data

Predicting the sale price

Loss function

Multivariable Linear Regression

Conclusion

Building a Decision Tree

The Data

Decision Trees

Data Preprocessing

Cost function

Using a prebuild Decision Tree model

Building your own Decision Tree

Evaluation

Sending your predictions to Kaggle

Conclusion

Acknowledgments

Color palette extraction with K-means clustering

Unsupervised Learning

The Data

What is K-Means Clustering?

Data Preprocessing

Implementing K-Means clustering

Evaluation

Conclusion

Movie review sentiment analysis with Naive Bayes

Dealing with Text

Naive Bayes

Implementing Multinomial Naive Bayes

Predicting sentiment

Conclusion

Music artist Recommender System using Stochastic Gradient Descent

User Ratings

Recommender Systems

Recommending music artists

Conclusion

Fashion product image classification using Neural Networks

Image Data

Neural Networks

Classifying Images

Improving the accuracy

Conclusion

Build a taxi driving agent in a post-apocalyptic world using Reinforcement Learning

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